Such state-of-the-art entity linking models [1]}, [2]} employ attention-based bag-of-words context model and pre-trained entity embeddings bootstrapped from word embeddings to assess topic level context compatibility. However, the latent entity type information in the immediate context of the mention is neglected. We suspect this may sometimes cause the models link mentions to incorrect entities with incorrect type. To verify this, we conduct error analysis of the well known DeepEDhttps://github.com/dalab/deep-ed model [1]} on the development set of AIDA-CoNLL [4]}, and found that more than half of their error cases fall into the category of type errors where the predicted entity's type is different from the golden entity's type, although some predictive contextual cue for them can be found in their local context.
As shown in Fig. REF , the full model of [1]} ganea2017deep incorrectly links the mention “Milwaukee" to the entity Milwaukee_Brewers. However, the preposition “In" is a strong cue predictive of the type (location) of mention “Milwaukee" which is helpful for disambiguation. The reason why the local context model of [1]} ganea2017deep couldn't capture such apparent cue is two folds. On one hand, the context encoding module adopts a bag-of-words encoding scheme which is position agnostic. As shown in Fig. REF (b), the attention mechanism is helpful for selecting predictive words (e.g. “Milwaukee", “games" etc.), but does not capture the pattern that the previous word “In" of the mention “Milwaukee" which very likely refers to an entity with location type.
On the other hand, the pre-trained entity embedding of [1]} ganea2017deep is not very sensitive to entity types.
For example, as shown in Table REF , when we query the most similar entities with the entity Steve_Jobs, the top one returned entity is Apple_Inc., which is a different type but releated at topic level. So it is natural for the model of [1]} ganea2017deep to make type errors when it is trained to fit such entity embeddings.
